| Hyperparameters and tuning strategies for random forest P Probst, MN Wright, AL Boulesteix Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1301, 2018 | 317 | 2018 |
| Random forest versus logistic regression: a large-scale benchmark experiment R Couronné, P Probst, AL Boulesteix BMC bioinformatics 19 (1), 1-14, 2018 | 203 | 2018 |
| Tunability: importance of hyperparameters of machine learning algorithms P Probst, B Bischl, AL Boulesteix Journal of Machine Learning Research 20 (53), 1-32, 2018 | 184 | 2018 |
| To Tune or Not to Tune the Number of Trees in Random Forest. P Probst, AL Boulesteix Journal of Machine Learning Research 18 (181), 1-18, 2018 | 163 | 2018 |
| ranger: A fast implementation of random forests MN Wright, S Wager, P Probst R package version 0.5. 0, URL https://CRAN. R-project. org/package= ranger, 2016 | 41 | 2016 |
| Multilabel classification with R package mlr P Probst, Q Au, G Casalicchio, C Stachl, B Bischl The R Journal 9 (1), 352--369, 2017 | 19 | 2017 |
| tscount: Analysis of Count Time Series T Liboschik, R Fried, K Fokianos, P Probst R package version 1 (0), 2016 | 13 | 2016 |
| Learning multiple defaults for machine learning algorithms F Pfisterer, JN van Rijn, P Probst, AC Müller, B Bischl Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 11 | 2021 |
| Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations AL Boulesteix, S Janitza, R Hornung, P Probst, H Busen, A Hapfelmeier Biometrical Journal 61 (5), 1314-1328, 2019 | 11 | 2019 |
| Large-scale benchmark study of survival prediction methods using multi-omics data M Herrmann, P Probst, R Hornung, V Jurinovic, AL Boulesteix Briefings in bioinformatics 22 (3), bbaa167, 2021 | 9 | 2021 |
| A fast implementation of random forests MN Wright, S Wager, P Probst R package version 0.11 2, 2019 | 9 | 2019 |
| Quality of life up to 10 years after traumatic brain injury: a cross-sectional analysis K Rauen, L Reichelt, P Probst, B Schäpers, F Müller, K Jahn, N Plesnila Health and quality of life outcomes 18, 1-12, 2020 | 8 | 2020 |
| Package ‘ranger’ MN Wright, S Wager, P Probst, MMN Wright | 7 | 2018 |
| Automatic exploration of machine learning experiments on openml D Kühn, P Probst, J Thomas, B Bischl arXiv preprint arXiv:1806.10961, 2018 | 6 | 2018 |
| Decompressive craniectomy is associated with good quality of life up to 10 years after rehabilitation from traumatic brain injury K Rauen, L Reichelt, P Probst, B Schäpers, F Müller, K Jahn, N Plesnila Critical care medicine 48 (8), 1157-1164, 2020 | 5 | 2020 |
| Hyperparameters, tuning and meta-learning for random forest and other machine learning algorithms P Probst lmu, 2019 | 4 | 2019 |
| Asthma features in severe COPD: Identifying treatable traits S Matthes, J Stadler, J Barton, G Leuschner, D Munker, P Arnold, ... Respiratory medicine 145, 89-94, 2018 | 4 | 2018 |
| mlr Tutorial J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ... arXiv preprint arXiv:1609.06146, 2016 | 3 | 2016 |
| Quality of life after traumatic brain injury: a cross-sectional analysis uncovers age-and sex-related differences over the adult life span K Rauen, CB Späni, MC Tartaglia, MT Ferretti, L Reichelt, P Probst, ... GeroScience 43 (1), 263-278, 2021 | 2 | 2021 |
| Impact of pathologic body composition assessed by CT-based anthropometric measurements in adult patients with multiple trauma: A retrospective analysis B Poros, T Irlbeck, P Probst, A Volkmann, P Paprottka, W Böcker, ... European Journal of Trauma and Emergency Surgery, 1-15, 2019 | 2 | 2019 |
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
